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Dr.Kumuda Gururao

I am an independent Consultant / Advisor for implementing ELearning / Social Media Learning, Online Marketing & Social Media Marketing for Corporates and Hr.Edu.Sectors. I have done my Ph.D. in Education specializing learning strategies and MBA in IT & Software Marketing and well versed in IT skills (Multimedia & Web based). For details visit: http://www.advisor2u.com

05/24/2017

Bridging Educational Data Mining and Learning Analytics

With the entry of machine learning, AI and deep learning in various fields, the field of education too is not lagging behind in embracing and implementing the applications of them.

I shall first explain what is educational data mining (EDM) and learning analytics (LA) and then proceed on to bridging EDM and LA.

‘Educational Data Mining (EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in’.

The environments from where the educational data is collected include classrooms where blended mode of teaching is offered, online education, social networks, online chats with humans and bots, online questionnaires, feedback, conferences / seminars etc (using xAPI)). The types of data collected include student profiles, interactive data that includes interaction with system, peers, faculty. These data must be visualized so that the objective of EDM, that is, to improve learning is realized.

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With the entry of machine learning, AI and deep learning in various fields, the field of education too is not lagging behind in embracing and implementing the applications of them.

I shall first explain what is educational data mining (EDM) and learning analytics (LA) and then proceed on to bridging EDM and LA.

‘Educational Data Mining (EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in’.

The environments from where the educational data is collected include classrooms where blended mode of teaching is offered, online education, social networks, online chats with humans and bots, online questionnaires, feedback, conferences / seminars etc (using xAPI)). The types of data collected include student profiles, interactive data that includes interaction with system, peers, faculty. These data must be visualized so that the objective of EDM, that is, to improve learning is realized.